ArticleBMC systems biology2016
In silico modeling for tumor growth visualization.
Article in BMC systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 25 citations in OpenAlex.
- Improving 3D deep learning segmentation with biophysically motivated cell synthesis.Communications biology · 2025Article
- Enhanced metabolomic predictions using concept drift analysis: identification and correction of confounding factors.Bioinformatics advances · 2025Article
- Review
- Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study.BMC bioinformatics · 2019Article
- Antibody-Drug Conjugates (ADC) Against Cancer Stem-Like Cells (CSC)-Is There Still Room for Optimism?Frontiers in oncology · 2019Review
- In silico cancer research towards 3R.BMC cancer · 2018Review
- An information model for computable cancer phenotypes.BMC medical informatics and decision making · 2016Article
- Virtual autopsy: Machine Learning and AI provide new opportunities for investigating minimal tumor burden and therapy resistance by cancer patients.Autopsy & case reportsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCancer is a complex disease. Fundamental cellular based studies as well as modeling provides insight into cancer biology and strategies to treatment of the disease. In silico models complement in vivo models. Research on tumor growth involves a plethora of models each emphasizing isolated aspects of benign and malignant neoplasms. Biologists and clinical scientists are often overwhelmed by the mathematical background knowledge necessary to grasp and to apply a model to their own research.
resultsWe aim to provide a comprehensive and expandable simulation tool to visualizing tumor growth. This novel Web-based application offers the advantage of a user-friendly graphical interface with several manipulable input variables to correlate different aspects of tumor growth. By refining model parameters we highlight the significance of heterogeneous intercellular interactions on tumor progression. Within this paper we present the implementation of the Cellular Potts Model graphically presented through Cytoscape.js within a Web application. The tool is available under the MIT license at https://github.com/davcem/cpm-cytoscape and http://styx.cgv.tugraz.at:8080/cpm-cytoscape/ .
conclusionIn-silico methods overcome the lack of wet experimental possibilities and as dry method succeed in terms of reduction, refinement and replacement of animal experimentation, also known as the 3R principles. Our visualization approach to simulation allows for more flexible usage and easy extension to facilitate understanding and gain novel insight. We believe that biomedical research in general and research on tumor growth in particular will benefit from the systems biology perspective.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.